Dr Hongbin Yang is now working as an algorithm scientist in Chemical.ai, and leading a team developing algorithms for retrosynthesis and lab automation since 2021.
After completing a BSc at East China University of Science and Technology (ECUST) in Shanghai, Hongbin continued his study at ECUST for a PhD titled In Silico Prediction of Chemical ADMET Properties via Statistics and Machine Learning Methods, during which Hongbin focused on structural alerts and QSAR techniques for toxicity prediction and toxicology research. He started his post-doctoral research project on computational toxicology in November 2019. Academic mentorship was provided by Dr Andreas Bender from University of Cambridge and additionally there was access to industry knowledge and resources of AstraZeneca and GSK.
2014-2019 East China University of Science and Technology, Pharmaceutical Science, Ph.D.
2010-2014 East China University of Science and Technology, Pharmaceutical Engineering, B.S.
2021-Now Developing algorithms for retro-synthesis and lab automation in Chemical.ai
2019-2021 Preidction of cardiovascular risk of compounds by both in vitro and in silico methods
2014-2019 Prediction of chemical toxicity via machine learning methods
2017-2018 Developing Web server for Prediction of chemical ADMET properties (admetSAR) and optimisation of chemical ADMET properties (ADMETopt)
2015-2016 Developing tools for QSAR models based on Orange and Scikit-Learn
2012-2014 Developing database and methodology for predicting drug-target interaction